"""simple docstring"""






def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase = 10							)				-> Optional[Any]:
				if not isinstance(lowerCamelCase_	,       lowerCamelCase_							) or n < 0:
								raise ValueError("Invalid input"							)
				SCREAMING_SNAKE_CASE__			=					10**n
				SCREAMING_SNAKE_CASE__			=					28_433 * (pow(2	,       7_830_457	,       lowerCamelCase_							)) + 1
				return str(number % modulus							)


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						print(F'{solution(1_0) = }')







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[int]								=	StableDiffusionInstructPixaPixPipeline
							UpperCAmelCase_						:		str								=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
							UpperCAmelCase_						:		List[Any]								=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							UpperCAmelCase_						:		Optional[int]								=	IMAGE_TO_IMAGE_IMAGE_PARAMS
							UpperCAmelCase_						:		Any								=	IMAGE_TO_IMAGE_IMAGE_PARAMS




							def       snake_case			(					self						)		->       List[Any]:
														torch.manual_seed(0						)
														A      :					Tuple      		=					UNetaDConditionModel(
														    block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=8    ,						out_channels=4    ,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,						cross_attention_dim=32    ,						)
														A      :					Any      		=					PNDMScheduler(skip_prk_steps=__UpperCAmelCase						)
														torch.manual_seed(0						)
														A      :					Dict      		=					AutoencoderKL(
														    block_out_channels=[32, 64]    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,						latent_channels=4    ,						)
														torch.manual_seed(0						)
														A      :					str      		=					CLIPTextConfig(
														    bos_token_id=0    ,						eos_token_id=2    ,						hidden_size=32    ,						intermediate_size=37    ,						layer_norm_eps=1E-05    ,						num_attention_heads=4    ,						num_hidden_layers=5    ,						pad_token_id=1    ,						vocab_size=10_00    ,						)
														A      :					Dict      		=					CLIPTextModel(__UpperCAmelCase						)
														A      :					Dict      		=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

														A      :					int      		=					{
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''safety_checker''': None,
														    '''feature_extractor''': None,
														}
														return components




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=0						)		->       Dict:
														A      :					Any      		=					floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)
														A      :					Dict      		=					image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
														A      :					Optional[Any]      		=					Image.fromarray(np.uinta(__UpperCAmelCase						)						).convert('''RGB'''						)
														if str(__UpperCAmelCase						).startswith('''mps'''						):
																					A      :					str      		=					torch.manual_seed(__UpperCAmelCase						)
														else:
																					A      :					Optional[Any]      		=					torch.Generator(device=__UpperCAmelCase						).manual_seed(__UpperCAmelCase						)
														A      :					List[Any]      		=					{
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''image''': image,
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 6.0,
														    '''image_guidance_scale''': 1,
														    '''output_type''': '''numpy''',
														}
														return inputs




							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					Optional[Any]      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					Dict      		=					self.get_dummy_components()
														A      :					str      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Any      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Optional[Any]      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					int      		=					sd_pipe(**__UpperCAmelCase						).images
														A      :					List[str]      		=					image[0, -3:, -3:, -1]
														assert image.shape == (1, 32, 32, 3)
														A      :					Optional[Any]      		=					np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       int:
														A      :					Any      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					Optional[Any]      		=					self.get_dummy_components()
														A      :					Any      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					str      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					str      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					Optional[int]      		=					'''french fries'''
														A      :					Any      		=					sd_pipe(**__UpperCAmelCase    ,						negative_prompt=__UpperCAmelCase						)
														A      :					Optional[int]      		=					output.images
														A      :					str      		=					image[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)
														A      :					Any      		=					np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Tuple:
														A      :					List[Any]      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					List[Any]      		=					self.get_dummy_components()
														A      :					Optional[int]      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Tuple      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					str      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					int      		=					[inputs['''prompt''']] * 2

														A      :					List[str]      		=					np.array(inputs['''image''']						).astype(np.floataa						) / 2_5_5.0
														A      :					Any      		=					torch.from_numpy(__UpperCAmelCase						).unsqueeze(0						).to(__UpperCAmelCase						)
														A      :					Union[str, Any]      		=					image / 2 + 0.5
														A      :					str      		=					image.permute(0    ,						3    ,						1    ,						2						)
														A      :					List[str]      		=					image.repeat(2    ,						1    ,						1    ,						1						)

														A      :					Union[str, Any]      		=					sd_pipe(**__UpperCAmelCase						).images
														A      :					int      		=					image[-1, -3:, -3:, -1]

														assert image.shape == (2, 32, 32, 3)
														A      :					Any      		=					np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Dict:
														A      :					int      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					List[Any]      		=					self.get_dummy_components()
														A      :					Union[str, Any]      		=					EulerAncestralDiscreteScheduler(
														    beta_start=0.0_0_0_8_5    ,						beta_end=0.0_1_2    ,						beta_schedule='''scaled_linear'''						)
														A      :					int      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Tuple      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Tuple      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					Optional[Any]      		=					sd_pipe(**__UpperCAmelCase						).images
														A      :					Dict      		=					image[0, -3:, -3:, -1]

														A      :					Optional[int]      		=					[round(__UpperCAmelCase    ,						4						) for x in image_slice.flatten().tolist()]
														print(''','''.join([str(__UpperCAmelCase						) for x in slice]						)						)

														assert image.shape == (1, 32, 32, 3)
														A      :					Dict      		=					np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Union[str, Any]:
														super().test_inference_batch_single_identical(expected_max_diff=3E-3						)






							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					int      		=					self.get_dummy_components()
														A      :					int      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Any      		=					VaeImageProcessor(do_resize=__UpperCAmelCase    ,						do_normalize=__UpperCAmelCase						)
														A      :					Optional[Any]      		=					pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					pipe(**self.get_dummy_inputs_by_type(__UpperCAmelCase    ,						input_image_type='''pt'''						)						)[0]

														A      :					Optional[Any]      		=					components['''vae''']
														A      :					Dict      		=					self.get_dummy_inputs_by_type(__UpperCAmelCase    ,						input_image_type='''pt'''						)

														for image_param in self.image_latents_params:
																					if image_param in inputs.keys():
																												A      :					Optional[Any]      		=					vae.encode(inputs[image_param]						).latent_dist.mode()

														A      :					List[str]      		=					pipe(**__UpperCAmelCase						)[0]

														A      :					Any      		=					np.abs(out - out_latents_inputs						).max()
														self.assertLess(__UpperCAmelCase    ,						1E-4    ,						'''passing latents as image input generate different result from passing image'''						)







@slow
@require_torch_gpu
class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       str:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def       snake_case			(					self    ,						__UpperCAmelCase=0						)		->       Tuple:
														A      :					List[str]      		=					torch.manual_seed(__UpperCAmelCase						)
														A      :					List[str]      		=					load_image(
														    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''						)
														A      :					Dict      		=					{
														    '''prompt''': '''turn him into a cyborg''',
														    '''image''': image,
														    '''generator''': generator,
														    '''num_inference_steps''': 3,
														    '''guidance_scale''': 7.5,
														    '''image_guidance_scale''': 1.0,
														    '''output_type''': '''numpy''',
														}
														return inputs




							def       snake_case			(					self						)		->       Optional[int]:
														A      :					Union[str, Any]      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					Optional[int]      		=					self.get_inputs()
														A      :					Any      		=					pipe(**__UpperCAmelCase						).images
														A      :					Union[str, Any]      		=					image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 5_12, 5_12, 3)
														A      :					Dict      		=					np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]						)

														assert np.abs(expected_slice - image_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Dict:
														A      :					str      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase						)
														A      :					List[str]      		=					LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					Union[str, Any]      		=					self.get_inputs()
														A      :					Union[str, Any]      		=					pipe(**__UpperCAmelCase						).images
														A      :					str      		=					image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 5_12, 5_12, 3)
														A      :					Dict      		=					np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]						)

														assert np.abs(expected_slice - image_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       List[Any]:
														A      :					List[Any]      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase						)
														A      :					List[str]      		=					DDIMScheduler.from_config(pipe.scheduler.config						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					str      		=					self.get_inputs()
														A      :					List[Any]      		=					pipe(**__UpperCAmelCase						).images
														A      :					str      		=					image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 5_12, 5_12, 3)
														A      :					Any      		=					np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]						)

														assert np.abs(expected_slice - image_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					0

														def callback_fn(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						) -> None:
																					A      :					List[Any]      		=					True
																					nonlocal number_of_steps
																					number_of_steps += 1
																					if step == 1:
																												A      :					Optional[Any]      		=					latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 64)
																												A      :					Union[str, Any]      		=					latents[0, -3:, -3:, -1]
																												A      :					List[str]      		=					np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]						)

																												assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2
																					elif step == 2:
																												A      :					int      		=					latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 64)
																												A      :					Optional[int]      		=					latents[0, -3:, -3:, -1]
																												A      :					str      		=					np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]						)

																												assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2

														A      :					Optional[Any]      		=					False

														A      :					str      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase    ,						torch_dtype=torch.floataa						)
														A      :					List[str]      		=					pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					List[Any]      		=					self.get_inputs()
														pipe(**__UpperCAmelCase    ,						callback=__UpperCAmelCase    ,						callback_steps=1						)
														assert callback_fn.has_been_called
														assert number_of_steps == 3




							def       snake_case			(					self						)		->       Tuple:
														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

														A      :					Optional[Any]      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase    ,						torch_dtype=torch.floataa						)
														A      :					Dict      		=					pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing(1						)
														pipe.enable_sequential_cpu_offload()

														A      :					Optional[Any]      		=					self.get_inputs()
														A      :					str      		=					pipe(**__UpperCAmelCase						)

														A      :					Dict      		=					torch.cuda.max_memory_allocated()
														# make sure that less than 2.2 GB is allocated
														assert mem_bytes < 2.2 * 10**9






							def       snake_case			(					self						)		->       int:
														A      :					Union[str, Any]      		=					self.get_inputs()
														# resize to resolution that is divisible by 8 but not 16 or 32
														A      :					Union[str, Any]      		=					inputs['''image'''].resize((5_04, 5_04)						)

														A      :					str      		=					'''timbrooks/instruct-pix2pix'''
														A      :					Tuple      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    __UpperCAmelCase    ,						safety_checker=__UpperCAmelCase    ,						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					Tuple      		=					pipe(**__UpperCAmelCase						)
														A      :					List[str]      		=					output.images[0]

														A      :					List[Any]      		=					image[2_55:2_58, 3_83:3_86, -1]

														assert image.shape == (5_04, 5_04, 3)
														A      :					Any      		=					np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-3






